인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
The ongoing Gaza War has resulted in significant loss of life and intensified an existing humanitarian crisis. Despite increasing demand for accurate data, mortality estimates remain challenging due to the inherent 'statistical fog of war'. Accurate quantification is hindered by incomplete reporting and uncertain age-sex distributions of casualties. Official death tolls are likely influenced by damaged infrastructure, security disruptions, and political motivations, complicating detailed demographic verification. Our study introduces a novel methodological approach-a Bayesian model incorporating novel priors-to explicitly account for measurement errors in mortality estimation by addressing reporting completeness and uncertainty in demographic distributions. We use these methods to estimate sex- and age-specific mortality patterns and associated life expectancy (LE) and LE losses due to direct conflict deaths from the Gaza War. We find that LE in Gaza was 42.3 (39.4-45.0) in 2023 and 40.4 (37.5-43.0) in 2024, corresponding to LE losses of 34.4 (31.7-37.3) and 36.4 (33.8-39.3) years, respectively, compared to a counterfactual scenario with no conflict-related deaths. This corresponds to 78,318 (70,614-87,504) conflict deaths by the end of 2024, reflecting a 14-fold increase in all-cause mortality during the conflict's first year. The age-sex pattern of Gaza's conflict deaths aligns with UN-IGME profiles from past genocides. To contextualize these estimates, we compare them with LE losses observed in the Gaza Strip, the West Bank, and all of Palestine between 2012 and 2019. Our estimates align with previously published work, after adjusting the reporting priors to ignore underreporting. Our versatile and robust framework for mortality estimation under conditions of data scarcity can inform future conflict research.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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